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Anomaly-Based Network Intrusion Detection: An Approach Using Ensemble-Based Machine Learning Algorithm


Kashif Gul Chachar and Syed Nadeem Ahsan


Vol. 24  No. 1  pp. 107-118


With the seamless growth of the technology, network usage requirements are expanding day by day. The majority of electronic devices are capable of communication, which strongly requires a secure and reliable network. Network-based intrusion detection systems (NIDS) is a new method for preventing and alerting computers and networks from attacks. Machine Learning is an emerging field that provides a variety of ways to implement effective network intrusion detection systems (NIDS). Bagging and Boosting are two ensemble ML techniques, renowned for better performance in the learning and classification process. In this paper, the study provides a detailed literature review of the past work done and proposed a novel ensemble approach to develop a NIDS system based on the voting method using bagging and boosting ensemble techniques. The test results demonstrate that the ensemble of bagging and boosting through voting exhibits the highest classification accuracy of 99.98% and a minimum false positive rate (FPR) on both datasets. Although the model building time is average which can be a tradeoff by processor speed.


Seamless, Intrusion detection, Anomaly, Ensemble, J48, Reptree, Na?ve-byes, Support Vector Machine (SVM).